Monitoring models after deployment
Practical strategies for monitoring performance, detecting drift, and setting useful alerts.
Monitor the service and the model
Track latency, errors and throughput as for any service, then add model-specific signals: input distributions, prediction distributions and, where labels arrive later, live accuracy.
Detect drift early
Compare recent inputs against a training baseline with simple statistical tests. Drift does not always mean degradation, but it tells you when to look.
Alert on what you will act on
Every alert should have an owner and a next step. Start with a few high-signal thresholds and tune them, rather than flooding the team with noise.
